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BACKGROUND: Among elementary-aged children (5-12yrs), summer vacation is associated with accelerated gains in Body Mass Index (BMI). A key behavioral driver of BMI gain is a lack of physical activity (PA). Previous studies indicate PA decreases during summer, compared to the school year but whether this difference is consistent among boys and girls, across age, and by income status remains unclear. This study examined differences in school and summer movement behaviors in a diverse cohort of children across three years. METHODS: Children (N = 1,203, age range 5-14 years, 48% girls) wore wrist-placed accelerometers for a 14-day wear-period during school (April/May) and summer (July) in 2021 to 2023, for a total of 6 timepoints. Mixed-effects models examined changes in school vs. summer movement behaviors (moderate-to-vigorous physical activity [MVPA], sedentary) for boys and girls, separately, and by age and household income groups (low, middle, and upper based on income-to-poverty ratio). RESULTS: Children provided a total of 35,435 valid days of accelerometry. Overall, boys (+ 9.1 min/day, 95CI 8.1 to 10.2) and girls (+ 6.2 min/day, 95CI 5.4 to 7.0) accumulated more MVPA during school compared to summer. Boys accumulated less time sedentary (-9.9 min/day, 95CI -13.0 to -6.9) during school, while there was no difference in sedentary time (-2.7 min/day, 95CI -5.7 to 0.4) for girls. Different patterns emerged across ages and income groups. Accumulation of MVPA was consistently greater during school compared to summer across ages and income groups. Generally, the difference between school and summer widened with increasing age, except for girls from middle-income households. Accumulation of sedentary time was higher during school for younger children (5-9yrs), whereas for older children (10-14yrs), sedentary time was greater during summer for the middle- and upper-income groups. For boys from low-income households and girls from middle-income households, sedentary time was consistently greater during summer compared to school across ages. CONCLUSIONS: Children are less active and more sedentary during summer compared to school, which may contribute to accelerated BMI gain. However, this differs by biological sex, age, and income. These findings highlight the complex factors influencing movement behaviors between school and summer.
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Acelerometría , Índice de Masa Corporal , Ejercicio Físico , Instituciones Académicas , Estaciones del Año , Humanos , Masculino , Femenino , Niño , Adolescente , Preescolar , Estudios de Cohortes , Conducta SedentariaRESUMEN
BACKGROUND: Lack of physical activity is a concern for children across diverse backgrounds, particularly affecting those in rural areas who face distinct challenges compared to their urban counterparts. Community-derived interventions are needed that consider the unique context and additional physical activity barriers in under-resourced rural settings. Therefore, a prospective pre-post pilot/feasibility study of Hoosier Sport was conducted over 8-weeks with 6th and 7th grade children in a low-socioeconomic rural middle school setting. The primary objective of the present study was to assess trial- and intervention-related feasibility indicators; and the secondary objective was to collect preliminary assessment data for physical activity levels, fitness, psychological needs satisfaction, and knowledge of physical activity and nutrition among participating youth. METHODS: This prospective 8-week pilot/feasibility study took place in the rural Midwestern United States where twenty-four middle school students participated in a mixed-methods pre-post intervention during physical education classes. The intervention included elements like sport-based youth development, individualized goal setting, physical activity monitoring, pedometer usage, and health education. Data were collected at baseline (T1) and post-intervention (T3), with intermediate measures during the intervention (T2). Qualitative data were integrated through semi-structured interviews. Analytical methods encompassed descriptive statistics, correlations, repeated measures ANOVA, and thematic analysis. RESULTS: Key findings indicate robust feasibility, with intervention-related scores (FIM, AIM, and IAM) consistently surpassing the "good" threshold and 100% retention and recruitment success. Additionally, participants showed significant physical performance improvement, shifting from the 25th to the 50th percentile in the 6-minute walk test (p < 0.05). Autonomy and competence remained high, reflecting positive perceptions of program practicality. Nutrition knowledge, initially low, significantly improved at post-intervention (p < 0.01), highlighting the efficacy of targeted nutritional education in Hoosier Sport. CONCLUSIONS: This study pioneers a community-engaged model for physical activity intervention in under-resourced rural settings. Positive participant feedback, coupled with improvements in physical fitness and psychosocial factors, highlights the potential of the co-design approach. The findings offer valuable insights and a practical template for future community-based research, signaling the promising impact of such interventions on holistic well-being. This research lays the foundation for subsequent phases of the ORBIT model, emphasizing collaborative, community-driven approaches to address the complex issue of declining physical activity levels among adolescents.
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Ejercicio Físico , Estudios de Factibilidad , Población Rural , Humanos , Proyectos Piloto , Masculino , Ejercicio Físico/psicología , Niño , Femenino , Adolescente , Estudios Prospectivos , Promoción de la Salud/métodos , Medio Oeste de Estados Unidos , Evaluación de Programas y Proyectos de Salud , Educación y Entrenamiento FísicoRESUMEN
STUDY OBJECTIVES: Evaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) heartrate in children during sleep. METHODS: Children (n=82, 61% male, 43.9% Black) wore a wrist-placed Apple Watch Series 7 (AWS7) and ActiGraph GT9X during a polysomnogram. 3-Axis accelerometry data was extracted from AWS7 and the GT9X. Accelerometry heartrate estimates were derived from jerk (the rate of acceleration change), computed using the peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from ECG traces were estimated from R-R intervals using R-pulse detection. Lin's Concordance Correlation Coefficient (CCC), mean absolute error (MAE) and mean absolute percent error (MAPE) assessed agreement with ECG estimated heartrate. Secondary analyses explored agreement by polysomnography sleep stage and a signal quality metric. RESULTS: The developed scripts are available on Github. For the GT9X, CCC was poor at -0.11 and MAE and MAPE were high at 16.8 (SD=14.2) beats/minute and 20.4% (SD=18.5%). For AWS7, CCC was moderate at 0.61 while MAE and MAPE were lower at 6.4 (SD=9.9) beats/minute and 7.3% (SD=10.3%). Accelerometry estimated heartrate for AWS7 was more closely related to ECG heartrate during N2, N3 and REM sleep than lights on, wake, and N1 and when signal quality was high. These patterns were not evident for the GT9X. CONCLUSIONS: Raw accelerometry data extracted from AWS7, but not the GT9X, can be used to estimate heartrate in children while they sleep. Future work is needed to explore the sources (i.e., hardware, software, etc.) of the GT9X's poor performance.
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Background: Preliminary studies (e.g., pilot/feasibility studies) can result in misleading evidence that an intervention is ready to be evaluated in a large-scale trial when it is not. Risk of Generalizability Biases (RGBs, a set of external validity biases) represent study features that influence estimates of effectiveness, often inflating estimates in preliminary studies which are not replicated in larger-scale trials. While RGBs have been empirically established in interventions targeting obesity, the extent to which RGBs generalize to other health areas is unknown. Understanding the relevance of RGBs across health behavior intervention research can inform organized efforts to reduce their prevalence. Purpose: The purpose of our study was to examine whether RGBs generalize outside of obesity-related interventions. Methods: A systematic review identified health behavior interventions across four behaviors unrelated to obesity that follow a similar intervention development framework of preliminary studies informing larger-scale trials (i.e., tobacco use disorder, alcohol use disorder, interpersonal violence, and behaviors related to increased sexually transmitted infections). To be included, published interventions had to be tested in a preliminary study followed by testing in a larger trial (the two studies thus comprising a study pair). We extracted health-related outcomes and coded the presence/absence of RGBs. We used meta-regression models to estimate the impact of RGBs on the change in standardized mean difference (ΔSMD) between the preliminary study and larger trial. Results: We identified sixty-nine study pairs, of which forty-seven were eligible for inclusion in the analysis (k = 156 effects), with RGBs identified for each behavior. For pairs where the RGB was present in the preliminary study but removed in the larger trial the treatment effect decreased by an average of ΔSMD=-0.38 (range - 0.69 to -0.21). This provides evidence of larger drop in effectiveness for studies containing RGBs relative to study pairs with no RGBs present (treatment effect decreased by an average of ΔSMD =-0.24, range - 0.19 to -0.27). Conclusion: RGBs may be associated with higher effect estimates across diverse areas of health intervention research. These findings suggest commonalities shared across health behavior intervention fields may facilitate introduction of RGBs within preliminary studies, rather than RGBs being isolated to a single health behavior field.
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INTRODUCTION: Physical activity (PA) promotion among school-aged youth is a global health priority. Recommendations for such promotion include implementing whole-of-school approaches that maximize resources across the school environment. This study examined schools' participation in an annual, government-led, and emirate-wide initiative in Dubai, called the Dubai Fitness Challenge, in which the goal is to accrue 30 minutes of PA every day for 30 days (as such, the initiative is colloquially referred to as "Dubai 30x30"). METHODS: A mixed-methods design was employed for this study. Three schools were recruited using convenience sampling. Participants were 18 physical education teachers, 20 classroom teachers, 2 principals and 45 students. Data sources included surveys, focus groups, and interviews. Data were analyzed using descriptive statistics, multinomial logistic regression, and open and axial coding to develop themes. RESULTS: School staff reported that most Dubai 30x30 activities were provided in physical education, at break times during school, and before and after school. Students reported that they mainly participated in Dubai 30x30 activities during physical education and occasionally participated in activities after school and on weekends. During school, students were more likely to reach higher PA intensity levels when they were in contexts other than the regular classroom setting. Among school staff, physical education teachers were most involved and classroom teachers were least involved in promoting Dubai 30x30. Parent engagement was high. Staff perceived that Dubai 30x30 brought the community together, but physical education teachers also indicated there was a lack of implementation guidance and they felt burdened. Participants believed Dubai 30x30 increased PA participation and helped to promote their schools. DISCUSSION: This study provides an initial glimpse into schools' participation in Dubai 30x30 and suggests that a whole-of-school PA lens is useful in gleaning information that could help to increase and optimize PA opportunities for students.
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Ejercicio Físico , Instituciones Académicas , Adolescente , Humanos , Niño , Estudiantes , Motivación , Grupos de Población , Servicios de Salud Escolar , Promoción de la Salud/métodosRESUMEN
Preliminary studies play a prominent role in the development of large-scale behavioral interventions. Though recommendations exist to guide the execution and interpretation of preliminary studies, these assume optimal scenarios which may clash with realities faced by researchers. The purpose of this study was to explore how principal investigators (PIs) balance expectations when conducting preliminary studies. We surveyed PIs funded by the National Institutes of Health to conduct preliminary behavioral interventions between 2000 and 2020. Four hundred thirty-one PIs (19% response rate) completed the survey (November 2021 to January 2022, 72% female, mean 21 years post-terminal degree). Most PIs were aware of translational models and believed preliminary studies should precede larger trials but also believed a single preliminary study provided sufficient evidence to scale. When asked about the relative importance of preliminary efficacy (i.e. changes in outcomes) and feasibility (i.e. recruitment, acceptance/adherence) responses varied. Preliminary studies were perceived as necessary to successfully compete for research funding, but among PIs who had peer-reviewed federal-level grants applications (n = 343 [80%]), responses varied about what should be presented to secure funding. Confusion surrounding the definition of a successful, informative preliminary study poses a significant challenge when developing behavior interventions. This may be due to a mismatch between expectations surrounding preliminary studies and the realities of the research enterprise in which they are conducted. To improve the quality of preliminary studies and advance the field of behavioral interventions, additional funding opportunities, more transparent criteria in grant reviews, and additional training for grant reviewers are suggested.
Initial testing of behavioral interventions can provide valuable information about the methods of the intervention and whether it is effective. However, recommendations that provide researchers with guidance on how to best conduct pilot studies assume ideal circumstances. The mismatch between what can be realistically accomplished in a preliminary study, and what researchers expect from preliminary studies creates confusion. As a result, it is difficult for researchers to judge the quality, relevance, and potential of preliminary studies. This study suggests more research funding opportunities, clearer rules for reviewing grant applications, and more training for the people who review these applications could help improve preliminary studies and create more effective health behavior programs.
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National Institutes of Health (U.S.) , Investigadores , Humanos , Estados Unidos , Femenino , Masculino , Encuestas y Cuestionarios , Terapia Conductista/métodos , Adulto , Persona de Mediana EdadRESUMEN
The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin's concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein.
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Acelerometría , Dispositivos Electrónicos Vestibles , Reproducibilidad de los Resultados , Ejercicio Físico , Monitores de EjercicioRESUMEN
INTRODUCTION: Current wearables that collect heart rate and acceleration were not designed for children and/or do not allow access to raw signals, making them fundamentally unverifiable. This study describes the creation and calibration of an open-source multichannel platform (PATCH) designed to measure heart rate and acceleration in children ages 3-8 yr. METHODS: Children (N = 63; mean age, 6.3 yr) participated in a 45-min protocol ranging in intensities from sedentary to vigorous activity. Actiheart-5 was used as a comparison measure. We calculated mean bias, mean absolute error (MAE) mean absolute percent error (MA%E), Pearson correlations, and Lin's concordance correlation coefficient (CCC). RESULTS: Mean bias between PATCH and Actiheart heart rate was 2.26 bpm, MAE was 6.67 bpm, and M%E was 5.99%. The correlation between PATCH and Actiheart heart rate was 0.89, and CCC was 0.88. For acceleration, mean bias was 1.16 mg and MAE was 12.24 mg. The correlation between PATCH and Actiheart was 0.96, and CCC was 0.95. CONCLUSIONS: The PATCH demonstrated clinically acceptable accuracies to measure heart rate and acceleration compared with a research-grade device.
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Frecuencia Cardíaca , Humanos , Niño , Preescolar , Frecuencia Cardíaca/fisiología , Masculino , Femenino , Calibración , Aceleración , Dispositivos Electrónicos Vestibles , Acelerometría/instrumentaciónRESUMEN
Background: Drivers of summer body mass index (BMI) gain in children remain unclear. The Circadian and Circannual Rhythm Model (CCRM) posits summer BMI gain is biologically driven, while the Structured Days Hypothesis (SDH) proposes it is driven by reduced structure. Objectives: Identify the mechanisms driving children's seasonal BMI gain through the CCRM and SDH. Methods: Children's (N = 147, mean age = 8.2 years) height and weight were measured monthly during the school year, and once in summer (July-August). BMI z-score (zBMI) was calculated using CDC growth charts. Behaviors were measured once per season. Mixed methods regression estimated monthly percent change in children's height (%HΔ), weight (%WΔ), and monthly zBMI for school year vs. summer vacation, seasonally, and during school months with no breaks vs. school months with a break ≥1 week. Results: School year vs. summer vacation analyses showed accelerations in children's %WΔ (Δ = 0.9, Standard Error (SE) = 0.1 vs. Δ = 1.4, SE = 0.1) and zBMI (Δ = -0.01, SE = 0.01 vs. Δ = 0.04, SE = 0.3) during summer vacation, but %HΔ remained relatively constant during summer vacation compared with school (Δ = 0.3, SE = 0.0 vs. Δ = 0.4, SE = 0.1). Seasonal analyses showed summer had the greatest %WΔ (Δ = 1.8, SE = 0.4) and zBMI change (Δ = 0.05, SE = 0.03) while %HΔ was relatively constant across seasons. Compared with school months without a break, months with a break showed higher %WΔ (Δ = 0.7, SE = 0.1 vs. Δ = 1.6, SE = 0.2) and zBMI change (Δ = -0.03, SE = 0.01 vs. Δ = 0.04, SE = 0.01), but %HΔ was constant (Δ = 0.4, SE = 0.0 vs. Δ = 0.3, SE = 0.1). Fluctuations in sleep timing and screen time may explain these changes. Conclusions: Evidence for both the CCRM and SDH was identified but the SDH may more fully explain BMI gain. Interventions targeting consistent sleep and reduced screen time during breaks from school may be warranted no matter the season.
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Obesidad Infantil , Aumento de Peso , Niño , Humanos , Índice de Masa Corporal , Estaciones del Año , Obesidad Infantil/epidemiología , Peso CorporalRESUMEN
INTRODUCTION: This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with a research-grade accelerometry. METHODS: Seventy-five 5- to 12-year-olds (58% male, 63% White) participated in a 60-min protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (COSMED K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity) were estimated via indirect calorimetry (criterion), and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph. RESULTS: Estimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95% confidence interval (CI), 67.1%-69.3%), 73.0% (95% CI, 71.8%-74.3%), and 66.6% (95% CI, 65.7%-67.5%), respectively, and weighted specificity = 84.4% (95% CI, 83.6%-85.2%), 82.0% (95% CI, 80.6%-83.4%), and 75.3% (95% CI, 74.7%-75.9%), respectively. Apple Watch produced the lowest mean bias (inactive, -4.0 ± 4.5; light activity, 2.1 ± 4.0) and absolute error (inactive, 4.9 ± 3.4; light activity, 3.6 ± 2.7) for inactive and light physical activity minutes. For moderate-to-vigorous physical activity, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent. CONCLUSIONS: Raw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than, a research-grade device, when compared with indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.
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Acelerometría , Dispositivos Electrónicos Vestibles , Niño , Humanos , Masculino , Femenino , Muñeca , Ejercicio Físico , Conducta SedentariaRESUMEN
We examined the comparability of children's nocturnal sleep estimates using accelerometry data, processed with and without a sleep log. In a secondary analysis, we evaluated factors associated with disagreement between processing approaches. Children (n = 722, age 5-12 years) wore a wrist-based accelerometer for 14 days during Autumn 2020, Spring 2021, and/or Summer 2021. Outcomes included sleep period, duration, wake after sleep onset (WASO), and timing (onset, midpoint, waketime). Parents completed surveys including children's nightly bed/wake time. Data were processed with parent-reported bed/wake time (sleep log), the Heuristic algorithm looking at Distribution of Change in Z-Angle (HDCZA) algorithm (no log), and an 8 p.m.-8 a.m. window (generic log) using the R-package 'GGIR' (version 2.6-4). Mean/absolute bias and limits of agreement were calculated and visualised with Bland-Altman plots. Associations between child, home, and survey characteristics and disagreement were examined with tobit regression. Just over half of nights demonstrated no difference in sleep period between sleep log and no log approaches. Among all nights, the sleep log approach produced longer sleep periods (9.3 min; absolute mean bias [AMB] = 28.0 min), shorter duration (1.4 min; AMB = 14.0 min), greater WASO (11.0 min; AMB = 15.4 min), and earlier onset (13.4 min; AMB = 17.4 min), midpoint (8.8 min; AMB = 15.3 min), and waketime (3.9 min; AMB = 14.8 min) than no log. Factors associated with discrepancies included smartphone ownership, bedroom screens, nontraditional parent work schedule, and completion on weekend/summer nights (range = 0.4-10.2 min). The generic log resulted in greater AMB among sleep outcomes. Small mean differences were observed between nights with and without a sleep log. Discrepancies existed on weekends, in summer, and for children with smartphones and screens in the bedroom.
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Background: Despite the widespread endorsement of 24-hour movement guidelines (physical activity, sleep, screentime) for youth, no standardized processes for categorizing guideline achievement exists. The purpose of this study was to illustrate the impact of different data handling strategies on the proportion of children meeting 24-hour movement guidelines (24hrG) and associations with overweight and obesity. Methods: A subset of 524 children (ages 5-12yrs) with complete 24-hour behavior measures on at least 10 days was used to compare the impact of data handling strategies on estimates of meeting 24hrG. Physical activity and sleep were measured via accelerometry. Screentime was measured via parent self-report. Comparison of meeting 24hrG were made using 1) average of behaviors across all days (AVG-24hr), 2) classifying each day and evaluating the percentage meeting 24hrG from 10-100% of their measured days (DAYS-24hr), and 3) the average of a random sample of 4 days across 10 iterations (RAND-24hr). A second subset of children (N=475) with height and weight data was used to explore the influence of each data handling strategy on children meeting guidelines and the odds of overweight/obesity via logistic regression. Results: Classification for AVG-24hr resulted in 14.7% of participants meeting 24hrG. Classification for DAYS-24hr resulted in 63.5% meeting 24hrG on 10% of measured days with <1% meeting 24hrG on 100% of days. Classification for RAND-24hr resulted in 15.9% of participants meeting 24hrG. Across 10 iterations, 63.6% of participants never met 24hrG regardless of the days sampled, 3.4% always met 24hrG, with the remaining 33.0% classified as meeting 24hrG for at least one of the 10 random iterations of days. Using AVG-24hr as a strategy, meeting all three guidelines associated with lower odds of having overweight obesity (OR=0.38, p<0.05). The RAND-24hr strategy produced a range of odds from 0.27 to 0.56. Using the criteria of needing to meet 24hrG on 100% of days, meeting all three guidelines associated with the lowest odds of having overweight and obesity as well (OR=0.04, p<0.05). Conclusions: Varying estimates of meeting the 24hrG and the odds of overweight and obesity results from different data handling strategies and days sampled.
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BACKGROUND: Guidelines, checklists, frameworks, and recommendations (GCFRs) related to preliminary studies serve as essential resources to assist behavioral intervention researchers in reporting findings from preliminary studies, but their impact on preliminary study reporting comprehensiveness is unknown. The purpose of this study was to conduct a scoping bibliometric review of recently published preliminary behavioral-focused intervention studies to (1) examine the prevalence of GCFR usage and (2) determine the associations between GCFR usage and reporting feasibility-related characteristics. METHODS: A systematic search was conducted for preliminary studies of behavioral-focused interventions published between 2018 and 2020. Studies were limited to the top 25 journals publishing behavioral-focused interventions, text mined to identify usage of GCFRs, and categorized as either not citing GCFRs or citing ≥ 2 GCFRs (Citers). A random sample of non-Citers was text mined to identify studies which cited other preliminary studies that cited GCFRs (Indirect Citers) and those that did not (Never Citers). The presence/absence of feasibility-related characteristics was compared between Citers, Indirect Citers, and Never Citers via univariate logistic regression. RESULTS: Studies (n = 4143) were identified, and 1316 were text mined to identify GCFR usage (n = 167 Citers). A random sample of 200 studies not citing a GCFR were selected and categorized into Indirect Citers (n = 71) and Never Citers (n = 129). Compared to Never Citers, Citers had higher odds of reporting retention, acceptability, adverse events, compliance, cost, data collection feasibility, and treatment fidelity (ORrange = 2.62-14.15, p < 0.005). Citers also had higher odds of mentioning feasibility in purpose statements, providing progression criteria, framing feasibility as the primary outcome, and mentioning feasibility in conclusions (ORrange = 6.31-17.04, p < 0.005) and lower odds of mentioning efficacy in purpose statements, testing for efficacy, mentioning efficacy in conclusions, and suggesting future testing (ORrange = 0.13-0.54, p < 0.05). Indirect Citers had higher odds of reporting acceptability and treatment fidelity (ORrange = 2.12-2.39, p < 0.05) but lower odds of testing for efficacy (OR = 0.36, p < 0.05) compared to Never Citers. CONCLUSION: The citation of GCFRs is associated with greater reporting of feasibility-related characteristics in preliminary studies of behavioral-focused interventions. Researchers are encouraged to use and cite literature that provides guidance on design, implementation, analysis, and reporting to improve the comprehensiveness of reporting for preliminary studies.
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BACKGROUND: The number of preliminary studies conducted and published has increased in recent years. However, there are likely many preliminary studies that go unpublished because preliminary studies are typically small and may not be perceived as methodologically rigorous. The extent of publication bias within preliminary studies is unknown but can prove useful to determine whether preliminary studies appearing in peer-reviewed journals are fundamentally different than those that are unpublished. The purpose of this study was to identify characteristics associated with publication in a sample of abstracts of preliminary studies of behavioral interventions presented at conferences. METHODS: Abstract supplements from two primary outlets for behavioral intervention research (Society of Behavioral Medicine and International Society of Behavioral Nutrition and Physical Activity) were searched to identify all abstracts reporting findings of behavioral interventions from preliminary studies. Study characteristics were extracted from the abstracts including year presented, sample size, design, and statistical significance. To determine if abstracts had a matching peer-reviewed publication, a search of authors' curriculum vitae and research databases was conducted. Iterative logistic regression models were used to estimate odds of abstract publication. Authors with unpublished preliminary studies were surveyed to identify reasons for nonpublication. RESULTS: Across conferences, a total of 18,961 abstracts were presented. Of these, 791 were preliminary behavioral interventions, of which 49% (388) were published in a peer-reviewed journal. For models with main effects only, preliminary studies with sample sizes greater than n = 24 were more likely to be published (range of odds ratios, 1.82 to 2.01). For models including interactions among study characteristics, no significant associations were found. Authors of unpublished preliminary studies indicated small sample sizes and being underpowered to detect effects as barriers to attempting publication. CONCLUSIONS: Half of preliminary studies presented at conferences go unpublished, but published preliminary studies appearing in peer-reviewed literature are not systematically different from those that remain unpublished. Without publication, it is difficult to assess the quality of information regarding the early-stage development of interventions. This inaccessibility inhibits our ability to learn from the progression of preliminary studies.
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GOAL AND AIMS: Evaluate the performance of a sleep scoring algorithm applied to raw accelerometry data collected from research-grade and consumer wearable actigraphy devices against polysomnography. FOCUS METHOD/TECHNOLOGY: Automatic sleep/wake classification using the Sadeh algorithm applied to raw accelerometry data from ActiGraph GT9X Link, Apple Watch Series 7, and Garmin Vivoactive 4. REFERENCE METHOD/TECHNOLOGY: Standard manual PSG sleep scoring. SAMPLE: Fifty children with disrupted sleep (M = 8.5 years, range = 5-12 years, 42% Black, 64% male). DESIGN: Participants underwent to single night lab polysomnography while wearing ActiGraph, Apple, and Garmin devices. CORE ANALYTICS: Discrepancy and epoch-by-epoch analyses for sleep/wake classification (devices vs. polysomnography). ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Equivalence testing for sleep/wake classification (research-grade actigraphy vs. commercial devices). CORE OUTCOMES: Compared to polysomnography, accuracy, sensitivity, and specificity were 85.5, 87.4, and 76.8, respectively, for Actigraph; 83.7, 85.2, and 75.8, respectively, for Garmin; and 84.6, 86.2, and 77.2, respectively, for Apple. The magnitude and trend of bias for total sleep time, sleep efficiency, sleep onset latency, and wake after sleep were similar between the research and consumer wearable devices. IMPORTANT ADDITIONAL OUTCOMES: Equivalence testing indicated that total sleep time and sleep efficiency estimates from the research and consumer wearable devices were statistically significantly equivalent. CORE CONCLUSION: This study demonstrates that raw acceleration data from consumer wearable devices has the potential to be harnessed to predict sleep in children. While further work is needed, this strategy could overcome current limitations related to proprietary algorithms for predicting sleep in consumer wearable devices.
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Acelerometría , Sueño , Humanos , Masculino , Niño , Femenino , Reproducibilidad de los Resultados , Polisomnografía , ActigrafíaRESUMEN
OBJECTIVES: Preliminary studies play a key role in developing large-scale interventions but may be held to higher or lower scientific standards during the peer review process because of their preliminary study status. STUDY DESIGN AND SETTING: Abstracts from 5 published obesity prevention preliminary studies were systematically modified to generate 16 variations of each abstract. Variations differed by 4 factors: sample size (n = 20 vs. n = 150), statistical significance (P < 0.05 vs. P > 0.05), study design (single group vs. randomized 2 groups), and preliminary study status (presence/absence of pilot language). Using an online survey, behavioral scientists were provided with a randomly selected variation of each of the 5 abstracts and blinded to the existence of other variations. Respondents rated each abstract on aspects of study quality. RESULTS: Behavioral scientists (n = 271, 79.7% female, median age 34 years) completed 1,355 abstract ratings. Preliminary study status was not associated with perceived study quality. Statistically significant effects were rated as more scientifically significant, rigorous, innovative, clearly written, warranted further testing, and had more meaningful results. Randomized designs were rated as more rigorous, innovative, and meaningful. CONCLUSION: Findings suggest reviewers place a greater value on statistically significant findings and randomized control design and may overlook other important study characteristics.
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Revisión por Pares , Proyectos de Investigación , Humanos , Femenino , Adulto , Masculino , Proyectos Piloto , PercepciónRESUMEN
Photoplethysmography (PPG) signal quality as a proxy for accuracy in heart rate (HR) measurement is useful in various public health contexts, ranging from short-term clinical diagnostics to free-living health behavior surveillance studies that inform public health policy. Each context has a different tolerance for acceptable signal quality, and it is reductive to expect a single threshold to meet the needs across all contexts. In this study, we propose two different metrics as sliding scales of PPG signal quality and assess their association with accuracy of HR measures compared to a ground truth electrocardiogram (ECG) measurement. METHODS: We used two publicly available PPG datasets (BUT PPG and Troika) to test if our signal quality metrics could identify poor signal quality compared to gold standard visual inspection. To aid interpretation of the sliding scale metrics, we used ROC curves and Kappa values to calculate guideline cut points and evaluate agreement, respectively. We then used the Troika dataset and an original dataset of PPG data collected from the chest to examine the association between continuous metrics of signal quality and HR accuracy. PPG-based HR estimates were compared with reference HR estimates using the mean absolute error (MAE) and the root-mean-square error (RMSE). Point biserial correlations were used to examine the association between binary signal quality and HR error metrics (MAE and RMSE). RESULTS: ROC analysis from the BUT PPG data revealed that the AUC was 0.758 (95% CI 0.624 to 0.892) for signal quality metrics of STD-width and 0.741 (95% CI 0.589 to 0.883) for self-consistency. There was a significant correlation between criterion poor signal quality and signal quality metrics in both Troika and originally collected data. Signal quality was highly correlated with HR accuracy (MAE and RMSE, respectively) between PPG and ground truth ECG. CONCLUSION: This proof-of-concept work demonstrates an effective approach for assessing signal quality and demonstrates the effect of poor signal quality on HR measurement. Our continuous signal quality metrics allow estimations of uncertainties in other emergent metrics, such as energy expenditure that relies on multiple independent biometrics. This open-source approach increases the availability and applicability of our work in public health settings.
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Fotopletismografía , Procesamiento de Señales Asistido por Computador , Frecuencia Cardíaca/fisiología , Algoritmos , ElectrocardiografíaRESUMEN
BACKGROUND: Pilot/feasibility or studies with small sample sizes may be associated with inflated effects. This study explores the vibration of effect sizes (VoE) in meta-analyses when considering different inclusion criteria based upon sample size or pilot/feasibility status. METHODS: Searches were to identify systematic reviews that conducted meta-analyses of behavioral interventions on topics related to the prevention/treatment of childhood obesity from January 2016 to October 2019. The computed summary effect sizes (ES) were extracted from each meta-analysis. Individual studies included in the meta-analyses were classified into one of the following four categories: self-identified pilot/feasibility studies or based upon sample size but not a pilot/feasibility study (N ≤ 100, N > 100, and N > 370 the upper 75th of sample size). The VoE was defined as the absolute difference (ABS) between the re-estimations of summary ES restricted to study classifications compared to the originally reported summary ES. Concordance (kappa) of statistical significance of summary ES between the four categories of studies was assessed. Fixed and random effects models and meta-regressions were estimated. Three case studies are presented to illustrate the impact of including pilot/feasibility and N ≤ 100 studies on the estimated summary ES. RESULTS: A total of 1602 effect sizes, representing 145 reported summary ES, were extracted from 48 meta-analyses containing 603 unique studies (avg. 22 studies per meta-analysis, range 2-108) and included 227,217 participants. Pilot/feasibility and N ≤ 100 studies comprised 22% (0-58%) and 21% (0-83%) of studies included in the meta-analyses. Meta-regression indicated the ABS between the re-estimated and original summary ES where summary ES ranged from 0.20 to 0.46 depending on the proportion of studies comprising the original ES were either mostly small (e.g., N ≤ 100) or mostly large (N > 370). Concordance was low when removing both pilot/feasibility and N ≤ 100 studies (kappa = 0.53) and restricting analyses only to the largest studies (N > 370, kappa = 0.35), with 20% and 26% of the originally reported statistically significant ES rendered non-significant. Reanalysis of the three case study meta-analyses resulted in the re-estimated ES rendered either non-significant or half of the originally reported ES. CONCLUSIONS: When meta-analyses of behavioral interventions include a substantial proportion of both pilot/feasibility and N ≤ 100 studies, summary ES can be affected markedly and should be interpreted with caution.
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Obesidad Infantil , Niño , Humanos , Estudios Epidemiológicos , Revisiones Sistemáticas como Asunto , Metaanálisis como AsuntoRESUMEN
This study gathered formative data on barriers to optimal child sleep to inform the development of a sleep intervention for parents of preschool-aged children in low-income households. Parents (n = 15, age: 34 ± 8 years, household income: $30,000 ± 17,845/year) reporting difficulties with their child's sleep participated in this study. Mixed methods included an online survey and semi-structured phone interview. Items assessed barriers/facilitators to optimal child sleep and intervention preferences. Interview transcripts were coded using inductive analyses and constant-comparison methods to generate themes. Derived themes were then mapped onto the Theoretical Domains Framework to contextualize barriers and inform future intervention strategies. Themes that emerged included: stimulating bedtime activities, child behavior challenges, variability in children's structure, parent work responsibilities, sleep-hindering environment, and parent's emotional capacity. Parent's intervention preferences included virtual delivery (preferred by 60% of parents) to reduce barriers and provide flexibility. Mixed preferences were observed for the group (47%) vs. individual (53%) intervention sessions. Parents felt motivated to try new intervention strategies given current frustrations, the potential for tangible results, and knowing others were in a similar situation. Future work will map perceived barriers to behavior change strategies using the Behavior Change Wheel framework to develop a parenting sleep intervention.
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Pobreza , Sueño , Preescolar , Humanos , Niño , Adulto , Responsabilidad Parental/psicología , Conducta Infantil , Encuestas y CuestionariosRESUMEN
Background: It is essential to quantify the accuracy and precision of bioelectrical impedance (BIA)-estimated percent body fat (%BF) to better interpret community-based research findings that utilize opportunistic measures. Methods: Study 1 measured the accuracy of a new dual-frequency foot-to-foot BIA device (Tanita DC-430U) compared with dual-energy X-ray absorptiometry (DXA) among healthy elementary school-aged children (N = 50). Study 2 examined the precision of BIA %BF estimates within and between days among children and adults (N = 38). Results: Regarding accuracy, Tanita DC-430U underestimated %BF by 8.0 percentage points compared with DXA (20.6% vs. 28.5%), but correctly ranked children in terms of %BF. Differences in %BF between BIA and DXA were driven by lower BIA-estimated fat mass (7.8 kg vs. 9.9 kg, p < 0.05) and higher BIA-estimated fat-free mass (25.3 kg vs. 24.1 kg, p < 0.05). The absolute agreement between BIA and DXA for estimated %BF was moderate (concordance correlation coefficients = 0.53). Regarding precision, measures taken at the same time, but on different days (root mean square standard deviation [RMSD] = 0.42-0.74) were more precise than the measures taken at different times within a single day (RMSD = 1.04-1.10). Conclusion: The Tanita DC-430U substantially underestimated %BF compared with DXA, highlighting the need to assess accuracy of new BIA devices when they are introduced to the market. Opportunistic measures of %BF estimates were most precise when taken at consistent times and in the morning, but may be utilized throughout the day with an understanding of within- and between-day variability.